Two different types of ensembles have been implemented in this study, to compare their performance on the metrics mentioned earlier. Each base member in the ensembles is a pre-trained ‘U-Net’ style neural network. A U-Net [1]} consists of an encoder part and a decoder part. The architecture of the decoder is a mirror image of the encoder, and this architecture is commonly referred to as the backbone. One can obtain different U-net styled models by using different backbones. In the first ensemble implemented in the work (EA), each base learner is a U-net with a different architecture. A candidate pool of pre-trained backbones is assembled for building the ensemble. The complete set of the candidates is provided in [2]}. A U-net model is initialized using each pre-trained encoder and a decoder with randomly initialized weights. Each of these U-nets are trained in parallel on the training dataset (with a 80%-20% split for training-validation). The hyper-parameters for the training process is provided in Table REF . A candidate model is added to the final ensemble if its accuracy score on the validation set, after convergence of the training, is greater than 97%. In addition, a second set of ensembles were designed by simply randomly initializing different decoder instances of a given model architecture. This set of ensembles is referred to as ER-I, with ‘I’ denoting the backbone for the base model. The different ensembles implemented in this study have been summarized in Table REF .
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